Ahsan Sheraz’s Post

🚀 pywho — a debugging painkiller for Python developers (30+ GitHub stars in 1 month 🔥) 💡 What is pywho? A zero-dependency Python CLI that explains your environment, traces imports, and detects module shadowing. No guessing. No scattered checks. Just clear answers. ⚠️ Pain point: Debugging Python issues usually means checking: • Interpreter • Virtualenv • sys.path • pip • Import resolution 👉 All separately → slow, repetitive, and perfect for “works on my machine” problems 📊 Existing tools: • Python built-in site/path inspection • pip debug • Manual import checks 👉 Useful individually, but each shows only part of the picture 🛠️ What pywho does: One CLI that gives you: ✅ Interpreter details ✅ Virtualenv detection ✅ Import tracing ✅ Import resolution insights ✅ Module shadow scanning ✅ JSON output for CI/sharing ➡️ One place, not five ➡️ Zero dependency ➡️ Cross-platform ➡️ Built for real debugging workflows 👨💻 For all Python developers 🔗 GitHub: https://lnkd.in/dMvz9PYM 🔗 PyPI: https://lnkd.in/dM72_8rs 🔗 Docs: https://lnkd.in/dCvUBAeu ♻️ Resharing to support the Python community 🤝 💬 What’s the most confusing Python environment issue you’ve debugged? #Python #PythonDeveloper #PythonDev #PyPI #PythonTools #DebuggingTools #DeveloperTools #DevTools #CLItools #CommandLine #SoftwareEngineering #BackendDevelopment #DevOps #OpenSource #OpenSourceProject #Programming #CodingLife #BuildInPublic #TechInnovation #ProductivityTools #Automation #CI_CD #TestingTools #PythonTips #CodeQuality #SoftwareDevelopment #DevelopersLife #TechCommunity #GitHubProjects

Are endless Python configurations slowing down your team and making project maintenance a nightmare? The days of complex, disjointed toolchains are officially over. It's time to elevate your workflow and achieve unparalleled efficiency! 😩 Introducing the Python project setup for 2026: a game-changing stack featuring `uv`, `Ruff`, `Ty`, and `Polars`. This isn't just an upgrade; it's a complete overhaul designed to deliver unparalleled speed, pristine code quality, and effortless maintainability, all unified under one roof. ✨ Imagine replacing multiple, disparate tools like pip, Black, and mypy with a single, integrated ecosystem that just *works*. This Astral-backed synergy simplifies everything from dependency management and lightning-fast linting to robust type checking and blazingly quick data processing for massive datasets. Revolutionize your development cycle and onboard new talent faster than ever before, ensuring your projects are cleaner and future-proof. 🚀 **Comment "PythonStack" to get the full article** Learn more about this streamlined Python project setup https://lnkd.in/gQQmtBnF 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘀𝗲𝗲 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘁𝗮𝗻𝗱𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗮𝗽𝗶𝗱𝗹𝘆 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝘄𝗼𝗿𝗹𝗱 𝗼𝗳 𝗔𝗜? 𝗧𝗮𝗸𝗲 𝗼𝘂𝗿 𝗾𝘂𝗶𝗰𝗸 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝘂𝗻𝗹𝗼𝗰𝗸 𝘆𝗼𝘂𝗿 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹! https://lnkd.in/g_dbMPqx #Python #DevOps #CleanCode #Programming #TechStack #SaizenAcuity

💡Python – Simple to Learn, Powerful to Build Python is one of the most beginner-friendly and powerful programming languages. Its clean syntax makes coding easy to read, write, and maintain, while its vast ecosystem allows developers to build anything from automation scripts to scalable web applications. To build strong Python skills for backend development with Django, Flask, and FastAPI, mastering key modules is essential. 🔹 Core Modules: os, sys, datetime, json, re, collections📐 🔹 Backend Utilities: logging, pathlib, functools, argparse 🔹 Web/API Modules: requests, hashlib, uuid, secrets🌐 🔹 Async Programming (FastAPI): asyncio, concurrent.futures🎯 🔹 Database Modules: sqlite3, sqlalchemy, psycopg2♟️🧩 With a solid understanding of these modules, developers can easily build REST APIs, automate tasks, manage databases, and develop scalable backend systems.🖥️🖲️ #Python #Django #Flask #FastAPI #BackendDevelopment #PythonDeveloper #APIDevelopment #SoftwareEngineering

🚀 Python Developers — Want to Level Up Faster? Stop waiting for the “perfect” project idea. Start building daily. 💡 Here’s a simple strategy: Build small, basic projects every day to sharpen your skills and grow your portfolio. 🔥 Why this works: • Consistency beats intensity • You learn by doing, not watching • Small wins build real confidence • Your portfolio grows automatically 🛠 Project ideas to get started: • Day 1: Calculator app • Day 2: Password generator • Day 3: To-do list (CLI or GUI) • Day 4: Web scraper • Day 5: API data fetcher • Day 6: File organizer script • Day 7: Mini game (like number guessing) 📈 In just 30 days, you’ll have: ✔ 30 real projects ✔ Stronger problem-solving skills ✔ A portfolio that actually stands out Don’t aim for perfection — aim for progress. Start today. Build daily. Grow faster. 💻✨ #Python #100DaysOfCode #LearnToCode #Developers #CodingJourney #PortfolioBuilding

I noticed that every time I start a new Python project, I repeat the same steps again and again. create virtual environment select python version setup basic project structure configure environment install dependencies It takes time, and each project setup becomes slightly different. If you have used npm, you know how simple it is to start a project. It works smoothly across many JavaScript frameworks with a consistent workflow. But in Python, setting up a new development environment is still not that simple. Each time we configure things manually, and the process is not standardized. So I built 𝗱𝗲𝘃𝗶𝘁-𝗰𝗹𝗶. It is a simple CLI tool that initializes a Python development environment in seconds. Just run: 𝗱𝗲𝘃𝗶𝘁 𝗶𝗻𝗶𝘁 During setup you can choose: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝘁𝘆𝗽𝗲 • Python Package • FastAPI • Django • AWS Scripts 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 • New venv • Existing Python interpreter • New conda • Existing conda env • Skip 𝗣𝘆𝘁𝗵𝗼𝗻 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 • for example 3.11 Reduce repetitive setup work and keep project structure consistent o7. 𝗣𝘆𝗣𝗜: https://lnkd.in/g2VzfWFy Feedback is welcome. #python #opensource #cli #developer #automation #devtools #productivity

🚀 Python for DevOps – Log Monitoring with File Output Today I built a simple automation script to read logs and write alerts to a separate file. 📂 Scenario: Instead of manually checking logs, automate detection of ERROR messages and store them in another file. 💻 Python Code: with open("app.log") as f, open("alerts.log", "w") as out: for line in f: if "ERROR" in line: out.write(line) output: root@satheesha:~# python3 Python 3.12.3 (main, Mar 3 2026, 12:15:18) [GCC 13.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> with open("app.log") as f, open("Alert.log", "w") as out: ...   for line in f: ...     if "ERROR" in line: ...       out.write(line) ...  17 >>> exit() root@satheesha:~# cat Alert.log ERROR: Disk full 🔍 What this does: Reads app.log line by line Filters only ERROR logs Writes them into alerts.log 📌 Why this is useful: Helps in faster troubleshooting Reduces manual log scanning Can be integrated with monitoring systems 🔥 Real DevOps Use Cases: Production log monitoring CI/CD pipeline validation Incident detection and alerting 📈 Next Step: Enhance this script to: Handle multiple log levels (ERROR / WARNING / INFO) Send alerts to email or Slack Monitor logs in real-time (like tail -f) #Python #DevOps #Automation #Scripting #Cloud #Learning #100DaysOfCode

🚀 From Scripts to Systems: A Python Automation Milestone Over the past few weeks, I’ve been deliberately strengthening my Python skills by focusing on real‑world automation, not just isolated scripts or tutorials. As a capstone, I recently completed an end‑to‑end, production‑style automation project, where I built a config‑driven Python system that: • Validates and processes structured CSV data • Applies configurable business rules (PAID / DUE classification) • Generates clean, reusable reports automatically • Integrates with an external API using retries and exponential backoff • Logs every critical step for observability • Persists execution state and run metrics in JSON • Is idempotent and safe to run repeatedly Throughout this journey, I focused heavily on engineering discipline: ✅ dry‑run mindset before writing data ✅ defensive validation of inputs ✅ separation of logic from configuration ✅ graceful failure handling instead of crashes ✅ building automation that can be trusted to run unattended This experience reinforced an important lesson for me: "Automation is not about writing code fast — it’s about building systems that behave correctly when things go wrong". I’m excited to continue building on this foundation as I move deeper into backend and automation‑heavy roles, and eventually into scalable application development. Always happy to connect and learn from others building reliable systems with Python. #Python #Automation #BackendDevelopment #SoftwareEngineering #LearningByBuilding #ResilientSystems #ContinuousLearning

I used to write Python scripts… Now I’m building tools. There’s a big difference 👇 👉 Script = runs once 👉 Tool = reusable, flexible, scalable 💡 Today I built my first CLI tool using Python And it completely changed how I see development. 📊 What I learned: • Accept input from terminal • Pass dynamic arguments • Run logic based on user input • Build reusable commands 💡 Real-world use case: Instead of editing code every time… 👉 I can now run: python app.py --category Electronics 👉 And get filtered results instantly Before this: ❌ Hardcoded values ❌ Manual changes ❌ Not reusable After this: ✅ Dynamic execution ✅ Flexible commands ✅ Developer-level workflow 💡 Biggest realization: Good developers don’t just write code… 👉 They build tools that others can use 📌 This is how real dev tools work: • Git • Docker • CLI utilities 👉 Everything starts from this concept 💬 Let’s discuss: Have you ever built or used a CLI tool that made your work easier? 🔥 Hashtags #Python #PythonTutorial #CLI #DeveloperTools #PythonDeveloper #Automation #BackendDevelopment #CodingJourney #LearnInPublic #DevelopersIndia #Tech #100DaysOfCode #BuildInPublic

🚀 **Day 29/30 – 30 Days of Python Project Challenge** Consistency builds skill. Skill builds confidence. 🚀 As part of my 30-day challenge, I’m focused on solving real-world problems while strengthening core development concepts. 🧠 Today’s Project: **Website Status Checker** I built a Python-based tool that monitors whether websites are **UP or DOWN** using HTTP requests, helping identify server issues quickly and efficiently. ✨ Why this project matters: In today’s digital world, uptime is critical. This project demonstrates how Python can be used to build simple monitoring tools that simulate real-world systems used in DevOps and backend operations. ⚙️ Key Features: 🌐 Multi-Website Monitoring: Check multiple URLs in one run 📊 Status Code Insights: Displays HTTP responses (200, 404, 500, etc.) 🎨 Colored Output: Uses Colorama for clear and readable terminal results ⚠️ Error Handling: Detects unreachable or invalid websites gracefully ⚡ Fast Execution: Lightweight and efficient with minimal setup 💡 Concepts Applied: HTTP Requests using Python (requests library) Exception Handling for robust error management Working with APIs and status codes Clean and readable terminal UI with color formatting Basic automation and monitoring concepts 🔗 GitHub: https://lnkd.in/dcDpkarZ 📌 Takeaway: Even simple scripts can solve real problems. Building tools that monitor systems is a powerful step toward understanding real-world software and infrastructure. On to Day 30. 🔥 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #Automation #DevOps #Backend #SoftwareDevelopment #Coding #Learning #OpenSource #Projects

🚀 Day 15: Testing in Python Writing code is important but making sure it works correctly is even more important. 👉 That’s where Testing comes in. Testing helps ensure that your code behaves as expected and reduces bugs in your applications. 🔹 Types of Testing: ✔ Unit Testing Testing individual parts (functions, methods) ✔ Integration Testing Testing how different parts work together 🔹 In Python, we commonly use: ✔ unittest (built-in library) ✔ pytest (popular third-party framework) 💡 Example (unittest): import unittest def add(a, b):     return a + b class TestAdd(unittest.TestCase):     def test_add(self):         self.assertEqual(add(2, 3), 5) 📌 Why it matters? ✔ Helps catch bugs early ✔ Improves code quality ✔ Makes your application more reliable ✔ Builds confidence when updating code 💡 Professionals don’t just write code they test it. 📈 Step by step, writing cleaner and more reliable software. #Python #Testing #Programming #Developers #SoftwareEngineering #BackendDevelopment #LearningJourney #Django